The Effects of Triclosan on Uropathogen Susceptibility to Clinically Relevant Antibiotics
Bibliographic record
Abstract
INTRODUCTION: Triclosan is a broad-spectrum antimicrobial agent currently used in numerous products including surgical scrubs and ureteral stents. Unfortunately, studies have shown triclosan resistance among several bacterial species. Our objective was to characterize resistance patterns of common uropathogens to triclosan and determine whether triclosan exposure would alter their susceptibility to common antibiotics. We hypothesized that triclosan exposure induces a metabolic stress rendering some bacterial strains more susceptible to other antibiotics. METHODS: Using largely clinical isolates comprising seven uropathogenic species, we conducted 24 hour growth experiments to determine triclosan minimal inhibitory concentrations (MIC) for each strain. Based upon these MICs, triclosan was added to agar plates at escalating sublethal concentrations and antibiotic disk diffusion assays were conducted using a range of clinically-relevant antibiotics. RESULTS: Varying susceptibility patterns were observed across all antibiotics studied. Several antibiotics demonstrated increased efficacy in conjunction with triclosan. The combined effect of triclosan with amoxicillin and gentamicin was superior when considering significant increases in susceptibility, with 6 (86%) and 5 (71%) of the 7 bacterial strains displaying enhanced sensitivity, respectively. The antimicrobial effects of nitrofurantoin and the fluoroquinolones were significantly enhanced for 4 (57%), 4 and 3 (42%) of the 7 pathogens, respectively. The two fluoroquinolones were the only antibiotics where susceptibility was negatively impacted (in one strain each) in combination with triclosan. CONCLUSIONS: The synergistic effects of triclosan and several antibiotics are consistent with a triclosan-dependent metabolic strain and/or membrane disruptive effect, and offers important insight into the combined use of antimicrobial compounds in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".